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The increasing number of benchmarks for Natural Language Processing (NLP)tasks in the computational job market domain highlights the demand for methodsthat can handle job-related tasks such as skill extraction, skillclassification, job title classification, and de-identification. While someapproaches have been developed that are specific to the job market domain,there is a lack of generalized, multilingual models and benchmarks for thesetasks. In this study, we introduce a language model called ESCOXLM-R, based onXLM-R, which uses domain-adaptive pre-training on the European Skills,Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27languages. The pre-training objectives for ESCOXLM-R include dynamic maskedlanguage modeling and a novel additional objective for inducing multilingualtaxonomical ESCO relations. We comprehensively evaluate the performance ofESCOXLM-R on 6 sequence labeling and 3 classification tasks in 4 languages andfind that it achieves state-of-the-art results on 6 out of 9 datasets. Ouranalysis reveals that ESCOXLM-R performs better on short spans and outperformsXLM-R on entity-level and surface-level span-F1, likely due to ESCO containingshort skill and occupation titles, and encoding information on theentity-level.

ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain